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Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of\n                    <jats:italic>Klebsiella pneumoniae<\/jats:italic>\n                    to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within \u00b11 two-fold dilution factor, is 92%. Individual accuracies are \u226590% for 15\/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete\n                    <jats:italic>in silico<\/jats:italic>\n                    MIC prediction panel for\n                    <jats:italic>K. pneumoniae<\/jats:italic>\n                    and provides a framework for building MIC prediction models for other pathogenic bacteria.\n                  <\/jats:p>","DOI":"10.1038\/s41598-017-18972-w","type":"journal-article","created":{"date-parts":[[2018,1,5]],"date-time":"2018-01-05T05:10:39Z","timestamp":1515129039000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":175,"title":["Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae"],"prefix":"10.1038","volume":"8","author":[{"given":"Marcus","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Brettin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3043-5307","authenticated-orcid":false,"given":"S. Wesley","family":"Long","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James M.","family":"Musser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Randall J.","family":"Olsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Olson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maulik","family":"Shukla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rick L.","family":"Stevens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangfang","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyunseung","family":"Yoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James J.","family":"Davis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,1,11]]},"reference":[{"key":"18972_CR1","doi-asserted-by":"crossref","unstructured":"Chen, L., Todd, R., Kiehlbauch, J., Walters, M. & Kallen, A. 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